The concept of “big data” is already well-known in the field of information technology. Big data is a collection of tools, techniques and approaches used when data sets are large and complex that it becomes difficult or impossible to store, query, analyze or process using current database management and data warehousing tools or traditional data processing applications. The challenge of handling big data include capture, organize, storage, search, sharing, analysis and visualization. The trend to larger data sets is due to the proliferation of data capture devices and the ease of capturing and entering data from a wide variety of sources.
There are various aspects related to the big data analytics enterprise framework which need to be still addressed in order to increase the expectation, granularity and comprehensiveness of the date in order to make the analysis of the data into useful, easy to handle and be cost-effective.
Few aspects which need to be addressed or taken into account are like
(a) Complexity, wherein a wide variety of different tools and techniques are needed to make Big Data Analytics work for an organization;
(b) Skill, wherein big data analytics requires unique programming and analysis skills that most programmers, developers, analysts and data scientists do not possess;
(c) Cost, wherein the demand for big data programming and analysis skills far outstrips supply, making people with such skills scarce and expensive;
(d) Time, wherein with the existing technology the time taken to perform the real time analytics on cloud is tough and cumbersome;
(e) Interdependency, wherein the software interdependency and appropriate resource unavailability makes the process of big data analytics very tedious;
(f) Inefficiency, wherein most of the tools for performing big data analytics is relatively new and people having those skill sets are facing difficulty in terms of high learning curve;
(g) Non availability, wherein there is non-availability of unified big data environments that allow big data storage & processing alongside predictive analytics functions
Existing big data analytics framework does have aspects such as complex, skill based, time consuming, interdependency, inefficient and non-availability listed above which does not help application developers, data scientists and system engineers.
Data has become a key asset for most modern day enterprises. Managing this data has become a major problem for the IT departments of these companies and organizations. For many years, the changes in business requirements have made it more and more difficult and expensive for enterprises to keep abreast of the changes in data—firstly, because of continuous changes in the tools and standards, and secondly because of the exponential increase in the amount of data that is being made available.
Hence, there is a need for a system and method for improved system and method for a big data analytics enterprise framework which simplifies big data analytics technologies for application developers, data scientists and system engineers.